Introduction:

Team clearly described the dataset and clearly described the motivation behind studying the data. Team provided scholarly citations or quantitative facts to describe the motivation.

Data Cleaning and Outlier Visualization:

Team clearly described their data cleaning and outlier removal process. Team presented insightful visualizations motivating to do further exploratory or confirmatory analysis.

#PART 1: Read csv, merge, clean and plot outliers.
library(readr)
library(readxl)
library(dplyr)
library(countrycode)
library(car)

source('Read_Clean.R')
cleaned <- Read_Clean()

#After Cleaning, check how many NA values are in dataset.
# cleaned %>%
#   select(everything()) %>%
#   summarise_all(funs(sum(is.na(.)))) %>% rowSums()

Dimension Reduction Analysis:

Team applied dimension reduction analysis correctly and discussed the motivation behind that. Also, they provided interesting insights into the results.

Part A: MDS

#PART 2: MDS
image

image

# PART 3: PCA
library(pryr)
library(ggbiplot) #if the library is not present use the code below
#library(devtools)
#install_github("vqv/ggbiplot")
source('PCA.R')
(PrinCompPlot <- PCA(cleaned))

# PART 3: Hierarchical Clustering between Continents
library(ape)
source('cluster_continents.R')
Cl_continents <- cluster_continents(cleaned)

# PART 4: K-means & Model Based Clustering between Countries
library(mclust)
library(maptools)
source('clusters_countries.R')
Cl_countries <- clusters_countries(cleaned)

#Show Centers
#PART 5: EFA
#PART 6: CFA